Principal component regression for data containing outliers and missing elements

Authors:
Sven Serneels;Tim Verdonck
Affiliations:
LS Services and Consultancy, Edegem, Belgium;Agoras Group, Department of Mathematics and Computer Science, University of Antwerp, Belgium
Venue:
Computational Statistics & Data Analysis
Year:
2009

Citing 4
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Abstract

A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope with missing elements in the data simultaneously. Simulations and an example illustrate the good statistical properties of the method.